Department of Science and Technology

Photoacoustic (PA) imaging, also called optoacoustic imaging, is a new biomedical imaging modality based on the use of laser-generated ultrasound that has emerged over the last decade. It is a hybrid modality, combining the high-contrast and spectroscopic-based specificity of optical imaging with the high spatial resolution of ultrasound imaging. In essence, a PA image can be regarded as an ultrasound image in which the contrast depends not on the mechanical and elastic properties of the tissue, but its optical properties, specifically optical absorption. As a consequence, it offers greater specificity than conventional ultrasound imaging with the ability to detect haemoglobin, lipids, water and other light-absorbing chomophores, but with greater penetration depth than purely optical imaging modalities that rely on ballistic photons. As well as visualizing anatomical structures such as the microvasculature, it can also provide functional information in the form of blood oxygenation, blood flow and temperature. All of this can be achieved over a wide range of length scales from micrometres to centimetres with scalable spatial resolution. These attributes lend PA imaging to a wide variety of applications in clinical medicine, preclinical research and basic biology for studying cancer, cardiovascular disease, abnormalities of the microcirculation and other conditions. With the emergence of a variety of truly compelling in vivo images obtained by a number of groups around the world in the last 2-3 years, the technique has come of age and the promise of PA imaging is now beginning to be realized. Recent highlights include the demonstration of whole-body small-animal imaging, the first demonstrations of molecular imaging, the introduction of new microscopy modes and the first steps towards clinical breast imaging being taken as well as a myriad of in vivo preclinical imaging studies. In this article, the underlying physical principles of the technique, its practical implementation, and a range of clinical and preclinical applications are reviewed.

INTRODUCTION Genetic interactions occur when mutations in two or more genes combine to generate an unexpected phenotype. An extreme negative or synthetic lethal genetic interaction occurs when two mutations, neither lethal individually, combine to cause cell death. Conversely, positive genetic interactions occur when two mutations produce a phenotype that is less severe than expected. Genetic interactions identify functional relationships between genes and can be harnessed for biological discovery and therapeutic target identification. They may also explain a considerable component of the undiscovered genetics associated with human diseases. Here, we describe construction and analysis of a comprehensive genetic interaction network for a eukaryotic cell. RATIONALE Genome sequencing projects are providing an unprecedented view of genetic variation. However, our ability to interpret genetic information to predict inherited phenotypes remains limited, in large part due to the extensive buffering of genomes, making most individual eukaryotic genes dispensable for life. To explore the extent to which genetic interactions reveal cellular function and contribute to complex phenotypes, and to discover the general principles of genetic networks, we used automated yeast genetics to construct a global genetic interaction network. RESULTS We tested most of the ~6000 genes in the yeast Saccharomyces cerevisiae for all possible pairwise genetic interactions, identifying nearly 1 million interactions, including ~550,000 negative and ~350,000 positive interactions, spanning ~90% of all yeast genes. Essential genes were network hubs, displaying five times as many interactions as nonessential genes. The set of genetic interactions or the genetic interaction profile for a gene provides a quantitative measure of function, and a global network based on genetic interaction profile similarity revealed a hierarchy of modules reflecting the functional architecture of a cell. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections associated with defects in cell cycle progression or cellular proteostasis. Importantly, the global network illustrates how coherent sets of negative or positive genetic interactions connect protein complex and pathways to map a functional wiring diagram of the cell. CONCLUSION A global genetic interaction network highlights the functional organization of a cell and provides a resource for predicting gene and pathway function. This network emphasizes the prevalence of genetic interactions and their potential to compound phenotypes associated with single mutations. Negative genetic interactions tend to connect functionally related genes and thus may be predicted using alternative functional information. Although less functionally informative, positive interactions may provide insights into general mechanisms of genetic suppression or resiliency. We anticipate that the ordered topology of the global genetic network, in which genetic interactions connect coherently within and between protein complexes and pathways, may be exploited to decipher genotype-to-phenotype relationships. A global network of genetic interaction profile similarities. ( Left ) Genes with similar genetic interaction profiles are connected in a global network, such that genes exhibiting more similar profiles are located closer to each other, whereas genes with less similar profiles are positioned farther apart. ( Right ) Spatial analysis of functional enrichment was used to identify and color network regions enriched for similar Gene Ontology bioprocess terms.

Ninety-two mixed etiology neurological patients and 216 control participants were assessed on a range of neuropsychological tests, including 10 neuropsychological measures of executive function derived from 6 different tests. People who knew the patients well (relatives or carers) completed a questionnaire about the patient's dysexecutive problems in everyday life, and this paper reports the extent to which the tests predicted the patients' everyday life problems. All of the tests were significantly predictive of at least some of the behavioral and cognitive deficits reported by patients' carers. However, factor analysis of the patients' dysexecutive symptoms suggested a fractionation of the dysexecutive syndrome, with neuropsychological tests loading differentially on 3 underlying cognitive factors (Inhibition, Intentionality, and Executive Memory), supporting the conclusions that different tests measure different cognitive processes, and that there may be limits to the fractionation of the executive system.

The aim of this paper is to propose a comprehensive framework for the management of innovation in construction, addressing the construction innovation problem in two distinctive ways at the institutional and rm levels.First, an institutional perspective derived from research on complex systems industries is developed which provides an alternative to the volume production model for construction innovation research.The roles of the innovation infrastructure, innovation superstructure and systems integrator are all identi ed and applied to construction.The paper then moves on to the rm level where the two key innovation dynamics the top-down adoption=implementation dynamic and the bottom up problem solving=learning dynamic are identi ed.The paper ends by calling for more case studies of the trajectories of construction innovations.L'objet de cet article est de proposer un cadre global ou ge rer l'innovation dans le secteur de la construction; l'auteur aborde la question de l'innovation sous deux angles diffe rents, au niveau des institutions et celui des industriels.En un premier temps, on de veloppe une perspective institutionelle de rive e de la recherche sur les syste mes complexes; on de bouche alors sur une alternative au mode le de volume de production applique a la recherche en matie re d'innovation dans la construction.Les ro les de l'infrastructure et de la superstructure de l'innovation et celcui de l'inte grateur de syste mes sont tous de nis et applique s a la construction.L'auteur passe ensuite au niveau de l'industriel et de nit les deux axes principaux de l'innovation, la dynamique descendante d'adoption=mise en uvre, d'une part et, d'autre part, la dynamique ascendante de re solution des proble mes et d'enseignment a en tirer, L'auteur demande, pour conclure, que soient pre sente s davantage de cas d'e tude portant sur les itine raires suivis par des innovations dans le secteur de la construction.

Abstract Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes 1,2 and molecular mechanisms that are often specific to cell type 3,4 . Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance ( P < 5 × 10 −8 ) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores 5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.

Peripheral pain pathways are activated by a range of stimuli. We used diphtheria toxin to kill all mouse postmitotic sensory neurons expressing the sodium channel Nav1.8. Mice showed normal motor activity and low-threshold mechanical and acute noxious heat responses but did not respond to noxious mechanical pressure or cold. They also showed a loss of enhanced pain responses and spontaneous pain behavior upon treatment with inflammatory insults. In contrast, nerve injury led to heightened pain sensitivity to thermal and mechanical stimuli indistinguishable from that seen with normal littermates. Pain behavior correlates well with central input from sensory neurons measured electrophysiologically in vivo. These data demonstrate that Na(v)1.8-expressing neurons are essential for mechanical, cold, and inflammatory pain but not for neuropathic pain or heat sensing.

We classify the empirical literature on the wage impact of immigration into three groups, where studies in the first two groups estimate different relative effects, and studies in the third group estimate the total effect of immigration on wages. We interpret the estimates obtained from the different approaches through the lens of the canonical model to demonstrate that they are not comparable. We then relax two key assumptions in this literature, allowing for inelastic and heterogeneous labor supply elasticities of natives and the "downgrading" of immigrants. “Downgrading” occurs when the position of immigrants in the labor market is systematically lower than the position of natives with the same observed education and experience levels. Downgrading means that immigrants receive lower returns to the same measured skills than natives when these skills are acquired in their country of origin. We show that heterogeneous labor supply elasticities, if ignored, may complicate the interpretation of wage estimates, and particularly the interpretation of relative wage effects. Moreover, downgrading may lead to biased estimates in those approaches that estimate relative effects of immigration, but not in approaches that estimate total effects. We conclude that empirical models that estimate total effects not only answer important policy questions, but are also more robust to alternative assumptions than models that estimate relative effects.

BACKGROUND: MicroRNA (miRNA) encoding genes are abundant in vertebrate genomes but very few have been studied in any detail. Bioinformatic tools allow prediction of miRNA targets and this information coupled with knowledge of miRNA expression profiles facilitates formulation of hypotheses of miRNA function. Although the central nervous system (CNS) is a prominent site of miRNA expression, virtually nothing is known about the spatial and temporal expression profiles of miRNAs in the brain. To provide an overview of the breadth of miRNA expression in the CNS, we performed a comprehensive analysis of the neuroanatomical expression profiles of 38 abundant conserved miRNAs in developing and adult zebrafish brain. RESULTS: Our results show miRNAs have a wide variety of different expression profiles in neural cells, including: expression in neuronal precursors and stem cells (for example, miR-92b); expression associated with transition from proliferation to differentiation (for example, miR-124); constitutive expression in mature neurons (miR-124 again); expression in both proliferative cells and their differentiated progeny (for example, miR-9); regionally restricted expression (for example, miR-222 in telencephalon); and cell-type specific expression (for example, miR-218a in motor neurons). CONCLUSION: The data we present facilitate prediction of likely modes of miRNA function in the CNS and many miRNA expression profiles are consistent with the mutual exclusion mode of function in which there is spatial or temporal exclusion of miRNAs and their targets. However, some miRNAs, such as those with cell-type specific expression, are more likely to be co-expressed with their targets. Our data provide an important resource for future functional studies of miRNAs in the CNS.

Measuring sustainability is not only a contentious issue, but one which has captured the attention of both academics and politicians since the late 1980s. A plethora of methods and approaches have been developed over the last decades or so, from rapid measurements as inputs to specific projects, to longer-term processes of research, monitoring and wider learning. Indicators have been, however, the most influential measuring tool of all and despite the fact that the tensions between expert-led and citizen-led models in their development have fuelled much debate in the literature. It has been suggested that integrating the two approaches would tap into various levels of ‘knowledge’ of sustainability and thus, be a better way of assessing sustainability. However, little is known of whether these ‘integrated’ sets of sustainability indicators work in practice, or indeed reflect the local perspectives, values and understandings of sustainability which they aim to represent. This paper aims to fill this gap. First, an ‘integrative’ set of indicators is designed and second, this is discussed with over 60 ‘sustainability experts’ and 130 residents living in three urban areas in the UK. It is found that the set of indicators is generally a good reflection of urban sustainability in these areas, however, people tend to assign different degrees of ‘importance’ to individual indicators, something which is little accounted for when measuring urban sustainability. The paper concludes that sustainability indicators are not isolated pieces of information, but manifestations of local underlying processes and interconnections that can be mapped and which have the potential to expand our understanding of local sustainability.

Mammals are able to navigate to hidden goal locations by direct routes that may traverse previously unvisited terrain. Empirical evidence suggests that this "vector navigation" relies on an internal representation of space provided by the hippocampal formation. The periodic spatial firing patterns of grid cells in the hippocampal formation offer a compact combinatorial code for location within large-scale space. Here, we consider the computational problem of how to determine the vector between start and goal locations encoded by the firing of grid cells when this vector may be much longer than the largest grid scale. First, we present an algorithmic solution to the problem, inspired by the Fourier shift theorem. Second, we describe several potential neural network implementations of this solution that combine efficiency of search and biological plausibility. Finally, we discuss the empirical predictions of these implementations and their relationship to the anatomy and electrophysiology of the hippocampal formation.

Synopsis Ornithischia is a familiar and diverse clade of dinosaurs whose global phylogeny has remained largely unaltered since early cladistic analyses in the mid 1980s. Current understanding of ornithischian evolution is hampered by a paucity of explicitly numerical phylogenetic analyses that consider the entire clade. As a result, it is difficult to assess the robustness of current phylogenetic hypotheses for Ornithischia and the effect that the addition of new taxa or characters is likely to have on the overall topology of the clade. The new phylogenetic analysis presented here incorporates a range of new basal taxa and characters in an attempt to rigorously test global ornithischian phylogeny. Parsimony analysis is carried out with 46 taxa and 221 characters. Although the strict component consensus tree shows poor resolution in a number of areas, application of reduced consensus methods provides a well‐resolved picture of ornithischian interrelationships. Surprisingly, Heterodontosauridae is placed as the most basal group of all well‐known ornithischians, phylogenetically distant from a stem‐defined Ornithopoda, creating a topology that is more congruent with the known ornithischian stratigraphical record. There is no evidence for a monophyletic ‘Fabrosauridae’, and Lesothosaurus (the best‐known ‘fabrosaur') occupies an unusual position as the most basal member of Thyreophora. Other relationships within Thyreophora remain largely stable. The primitive thyreophoran Scelidosaurus is the sister taxon of Eurypoda (stegosaurs and ankylosaurs), rather than a basal ankylosaur as implied by some previous studies. The taxonomic content of Ornithopoda differs significantly from previous analyses and basal relationships within the clade are weakly supported, requiring further investigation. ‘Hypsilopho‐dontidae’ is paraphyletic, with some taxa (Agilisaurus, Hexinlusaurus, Othnielia) placed outside of Ornithopoda as non‐cerapodans. Ceratopsia and Pachycephalosauria are monophyletic and are united as Marginocephalia; however, the stability of these clades is reduced by a number of poorly preserved basal taxa. This analysis reaffirms much of the currently accepted ornithischian topology. Nevertheless, instability in the position and content of several clades (notably Heterodontosauridae and Ornithopoda) indicates that considerable future work on ornithischian phylogeny is required and causes problems for several current phylogenetic definitions.

ABSTRACT This paper explores how people from low‐income, minority ethnic groups perceive and experience exclusion from informal science education (ISE) institutions, such as museums and science centers. Drawing on qualitative data from four focus groups, 32 interviews, four accompanied visits to ISE institutions, and field notes, this paper presents an analysis of exclusion from science learning opportunities during visits alongside participants’ attitudes, expectations, and conclusions about participation in ISE. Participants came from four community groups in central London: a Sierra Leonean group ( n = 21), a Latin American group ( n = 18), a Somali group ( n = 6), and an Asian group ( n = 13). Using a theoretical framework based on the work of Bourdieu, the analysis suggests ISE practices were grounded in expectations about visitors’ scientific knowledge, language skills, and finances in ways that were problematic for participants and excluded them from science learning opportunities. It is argued that ISE practices reinforced participants preexisting sense that museums and science centers were “not for us.” The paper concludes with a discussion of the findings in relation to previous research on participation in ISE and the potential for developing more inclusive informal science learning opportunities.

Crop populations derived from experimental crosses enable the genetic dissection of complex traits and support modern plant breeding. Among these, multi-parent populations now play a central role. By mixing and recombining the genomes of multiple founders, multi-parent populations combine many commonly sought beneficial properties of genetic mapping populations. For example, they have high power and resolution for mapping quantitative trait loci, high genetic diversity and minimal population structure. Many multi-parent populations have been constructed in crop species, and their inbred germplasm and associated phenotypic and genotypic data serve as enduring resources. Their utility has grown from being a tool for mapping quantitative trait loci to a means of providing germplasm for breeding programmes. Genomics approaches, including de novo genome assemblies and gene annotations for the population founders, have allowed the imputation of rich sequence information into the descendent population, expanding the breadth of research and breeding applications of multi-parent populations. Here, we report recent successes from crop multi-parent populations in crops. We also propose an ideal genotypic, phenotypic and germplasm 'package' that multi-parent populations should feature to optimise their use as powerful community resources for crop research, development and breeding.

To combat infection and antimicrobial resistance, it is helpful to elucidate drug mechanism(s) of action. Here we examined how the widely used antimicrobial polyhexamethylene biguanide (PHMB) kills bacteria selectively over host cells. Contrary to the accepted model of microbial membrane disruption by PHMB, we observed cell entry into a range of bacterial species, and treated bacteria displayed cell division arrest and chromosome condensation, suggesting DNA binding as an alternative antimicrobial mechanism. A DNA-level mechanism was confirmed by observations that PHMB formed nanoparticles when mixed with isolated bacterial chromosomal DNA and its effects on growth were suppressed by pairwise combination with the DNA binding ligand Hoechst 33258. PHMB also entered mammalian cells, but was trapped within endosomes and excluded from nuclei. Therefore, PHMB displays differential access to bacterial and mammalian cellular DNA and selectively binds and condenses bacterial chromosomes. Because acquired resistance to PHMB has not been reported, selective chromosome condensation provides an unanticipated paradigm for antimicrobial action that may not succumb to resistance.

The difficult relationship between science, citizen science, and mass communication. A negative example,

Public Participation GIS (PPGIS) is a field of research that, among other things, focuses on the use of GIS by non-experts and occasional users. These users tend to have a diverse range of computer literacy, world views, cultural backgrounds and knowledge. These aspects require that the systems used within PPGIS are accessible and easy to use. Human-Computer Interaction (HCI) and the related usability evaluation techniques focus on how to make computer systems more accessible, while focusing on user needs and requirements. Thus, the synergy between PPGIS and HCI seems natural. In this paper, we discuss the aspects of this synergy, building on our experience from three workshops. We demonstrate how usability evaluation can contribute to PPGIS research, and how PPGIS research can contribute to the HCI aspects of GIS in general. We conclude this paper with a call for a user-centred design approach to PPGIS projects.

We present crustal thickness and Poisson's ratio determinations from receiver function analyzes at 32 sites on the Archaean and Proterozoic terrains of South India. The crustal thickness in the late Archaean (2.5 Ga) Eastern Dharwar Craton varies from 34–39 km. Similar crustal thickness is observed beneath the Deccan Volcanic Province and the Cuddapah basin. The most unexpected result is the anomalous present‐day crustal thickness of 42–51 km beneath the mid‐Archaean (3.4–3.0 Ga) segment of the Western Dharwar Craton. Since the amphibolite‐grade metamorphic mineral assemblages (5–7 Kbar paleopressures) in this part of Western Dharwar Craton equilibrated at the depths of 15–20 km, our observations suggest the existence of an exceptionally thick (57–70 km) crust 3.0 Ga ago. Beneath the exhumed granulite terrain in southernmost India, the crustal thickness varies between 42–60 km. The Poisson's ratio ranges between 0.24–0.28 beneath the Precambrian terrains, indicating the presence of intermediate rock type in the lower crust. These observations of thickened crust suggest significant crustal shortening in South India during the Archaean.

The inapplicability of amino acid covariation methods to small protein families has limited their use for structural annotation of whole genomes. Recently, deep learning has shown promise in allowing accurate residue-residue contact prediction even for shallow sequence alignments. Here we introduce DMPfold, which uses deep learning to predict inter-atomic distance bounds, the main chain hydrogen bond network, and torsion angles, which it uses to build models in an iterative fashion. DMPfold produces more accurate models than two popular methods for a test set of CASP12 domains, and works just as well for transmembrane proteins. Applied to all Pfam domains without known structures, confident models for 25% of these so-called dark families were produced in under a week on a small 200 core cluster. DMPfold provides models for 16% of human proteome UniProt entries without structures, generates accurate models with fewer than 100 sequences in some cases, and is freely available.

There is a growing demand to integrate biosensors with microfluidics to provide miniaturized platforms with many favorable properties, such as reduced sample volume, decreased processing time, low cost analysis and low reagent consumption. These microfluidics-integrated biosensors would also have numerous advantages such as laminar flow, minimal handling of hazardous materials, multiple sample detection in parallel, portability and versatility in design. Microfluidics involves the science and technology of manipulation of fluids at the micro- to nano-liter level. It is predicted that combining biosensors with microfluidic chips will yield enhanced analytical capability, and widen the possibilities for applications in clinical diagnostics. The recent developments in microfluidics have helped researchers working in industries and educational institutes to adopt some of these platforms for point-of-care (POC) diagnostics. This review focuses on the latest advancements in the fields of microfluidic biosensing technologies, and on the challenges and possible solutions for translation of this technology for POC diagnostic applications. We also discuss the fabrication techniques required for developing microfluidic-integrated biosensors, recently reported biomarkers, and the prospects of POC diagnostics in the medical industry.

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